• Substantive research interests
  • Broader question: Emergence of AfD as party and parliamentary presence - what are the effects on party competition and parliamentarism?
  • Descriptive (preliminary) question: What are the prevalent framings in speeches given by AfD parliamentarians?
  • Contagion hypothesis (diffusion): (Speakers of) other parliamentary groups may take over framings offered by AfD speakers.
  • cp. DFG-Projekt “The populist challenge in parliament” (2019-2021, cooperation with Christian Strecker, Marcel Lewandowsky, Jochen Müller)
• Methodological interests
  • Validity and intersubjectivity of data-driven, “distant reading” approaches (in the eHumanities)
  • ML/AI: Annotation to gain training data for statistical learning => gold standard annotation
  • Social sciences: Traditions of coding and annotating text data: Quantitative/qualitative content analysis

• Combining distant and close reading is an unfulfilled promise: Software often inhibits combining both perspectives. How to implement workflows for coding and annotating textual data? The polmineR R package is presented as potential solution.

• Special focus: Interactive graph annotation as an approach to generate intersubjectively shared interpretations/understandings of discourse patterns.

• Schedule:
  • Theory is code
  • The MigParl corpus
  • AfD Keywords
  • Graph annotation
  • Conclusions

• Why R?
  • the most common programming language in the social sciences
  • comprehensive availability of statistical methods
  • great visualisation capabilites
  • usability: RStudio as IDE
  • reproducible research: Rmarkdown notebooks
• Why the Corpus Workbench (CWB)?
  • a classic toolset for corpus analysis
  • indexing and compression of corpora => performance
  • powerful and versatile syntax of the Corpus Query Processor (CQP)
  • permissive license (GPL)
• NoSQL / Lucene / Elasticsearch are potential alternatives - but not for now

The core family of packages:

• polmineR: basic vocabulary for corpus analysis

• RcppCWB: wrapper for the Corpus Workbench (using C++/Rcpp, follow-up on rcqp-package)

• cwbtools: tools to create and manage CWB indexed corpora

And there are a few other packages:

• GermaParl: documents and disseminates GermaParl corpus
• frappp: framework for parsing plenary protocols
• annolite: light-weight fulltext display and annotation tool
• topicanalysis: workflows with quantitative/qualitative elements for topic models
• gradget: graph annotation widget

• Getting started with polmineR is easy: Assuming that R and RStudio are installed, polmineR can be installed as simple as follows (dependencies such as RcppCWB will be installed automatically). Type in an R session:

install.packages("polmineR")

• Get the GermaParl corpus (corpus of plenary debates in the German Bundestag).

drat::addRepo("polmine") # add CRAN-style repository to known repos
install.packages("GermaParl") # the downloaded package includes a small sample dataset
GermaParl::germaparl_download_corpus() # get the full corpus

• That’s it. Ready to go.

library(polmineR)
use("GermaParl") # activate the corpora in the GermaParl package, i.e. GERMAPARL

• This is the “good old” workflow to create partitions (i.e. subcorpora):

p <- partition("GERMAPARL", year = 2001)
m <- partition("GERMAPARL", speaker = "Merkel", regex = TRUE)

• And there is an emerging new workflow …

am <- corpus("GERMAPARL") %>% subset(speaker == "Angela Merkel")

m <- corpus("GERMAPARL") %>% subset(grep("Merkel", speaker)) # Petra Merkel!

cdu_csu <- corpus("GERMAPARL") %>%
  subset(party %in% c("CDU", "CSU")) %>%
  subset(role != "presidency")

• You might read the code aloud as follows: “We generate a subcorpus X by taking the corpus GERMAPARL, subsetting it based on criterion Y, …”

dt <- dispersion("GERMAPARL", query = "Flüchtlinge", s_attribute = "year")
barplot(height = dt$count, names.arg = dt$year, las = 2, ylab = "Häufigkeit")

q <- '[pos = "NN"] "mit" "Migrationshintergrund"'
corpus("GERMAPARL") %>% kwic(query = q, cqp = TRUE, left = 10, right = 10)

kwic("GERMAPARL", query = "Islam", positivelist = c(good, bad)) %>%
  highlight(lightgreen = good, orange = bad) %>%
  tooltips(setNames(SentiWS[["word"]], SentiWS[["weight"]])) %>%
  knit_print()

• This is how you can recover the fulltext of a subcorpus.

corpus("GERMAPARL") %>% # take the GERMAPARL corpus
  subset(date == "2009-11-10") %>% # create a subcorpus based on a date
  subset(speaker == "Merkel") %>% # get me the speech given by Merkel
  html(height = "250px") %>% # turn it into html
  highlight(list(yellow = c("Bundestag", "Regierung"))) # and highlight words of interest

• Inspecting the fulltext can be extremely useful to evaluate topic models: This is how you would highlight the most likely terms of a topicmodel using polmineR:

h <- get_highlight_list(BE_lda, partition_obj = ek, no_token = 150)
h <- lapply(h, function(x) x[1:8])

corpus("BE") %>%
  subset(date == "2005-04-28") %>%
  subset(grepl("Körting", speaker)) %>% 
  as.speeches(s_attribute_name = "speaker", verbose = FALSE)[[4]] %>% 
  html(height = "350px") %>%
  highlight(highlight = h)

• The following analysis is based on the MigTex corpus.

• The corpus has been prepared in the MigTex Project (“Textressourcen für die Migrations- und Integrationsforschung”, funding: BMFSFJ)

• Preparation of all plenary debates in Germany’s regional parliaments (2000-2018) using the “Framework for Parsing Plenary Protocols” (frappp-package)

• Extraction of a thematic subcorpus using unsupervised learning (topic modelling)

• Size of the MigParl corpus: 27241205 tokens

• size without interjections and presidency: 22837376

• structural annotation: id | speaker | party | role | lp | session | date | regional_state | interjection | year | agenda_item | agenda_item_type | speech | topics | harmonized_topics

As announced initially, our analytical concern is speeches given by AfD parliamentarians.

• The polmineR package offers the functionality to get the cooccurrences for a specific query of interest. The default method for calculating cooccurrences is the loglikelihood test.

• The cooccurrences()-method can be applied to subcorpora / partitions, and corpora.

cooccurrences("GERMAPARL", query = 'Islam', left = 10, right = 10)

Starting with polmineR v0.7.9.11, the package includes a method to efficiently calculate all cooccurrences in a corpus.

m <- partition("GERMAPARL", year = 2008, speaker = "Angela Merkel", interjection = F)
drop <- terms(m, p_attribute = "word") %>% noise() %>% unlist()
Cooccurrences(m, p_attribute = "word", left = 5L, right = 5L, stoplist = drop) %>% 
  decode() %>% # 
  ll() %>%
  subset(ll >= 10.83) %>%
  subset(ab_count >= 5) -> coocs

So ‘gradgets’ are the solution suggested here. The links to the following three gradgets offer a visualisation that is interactive in a double sense:

1. You can turn the visualisation in three-dimensional space
2. You can click on the edges and nodes, get the concordances that are behind the statistical evaluation, and leave an annotation.

In a real-world workflow, the result of the graph annotation exercise can be stored and put into an online appendix to a publication that explains interpretative results.

So these are the gradgets:

• threedimensional gradget I
• threedimensional, anaglyph gradget II
• threedimensional, anaglyph gradget III (restrictive filtering)